Learning to Estimate 3D Human Pose from Point Cloud

3D pose estimation is a challenging problem in computer vision. Most of the existing neural-network-based approaches address color or depth images through convolution networks (CNNs). In this paper, we study the task of 3D human pose estimation from depth images. Different from the existing CNN-based human pose estimation method, we propose a deep human pose network for 3D pose estimation by taking the point cloud data as input data to model the surface of complex human structures. We first cast the 3D human pose estimation from 2D depth images to 3D point clouds and directly predict the 3D joint position. Our experiments on two public datasets show that our approach achieves higher accuracy than previous state-of-art methods. The reported results on both ITOP and EVAL datasets demonstrate the effectiveness of our method on the targeted tasks

Distributed Invariant Kalman Filter for Cooperative Localization using Matrix Lie Groups

This paper studies the problem of Cooperative Localization (CL) for multi-robot systems, where a group of mobile robots jointly localize themselves by using measurements from onboard sensors and shared information from other robots. We propose a novel distributed invariant Kalman Filter (DInEKF) based on the Lie group theory, to solve the CL problem in a 3-D environment. Unlike the standard EKF which computes the Jacobians based on the linearization at the state estimate, DInEKF defines the robots' motion model on matrix Lie groups and offers the advantage of state estimate-independent Jacobians. This significantly improves the consistency of the estimator. Moreover, the proposed algorithm is fully distributed, relying solely on each robot's ego-motion measurements and information received from its one-hop communication neighbors. The effectiveness of the proposed algorithm is validated in both Monte-Carlo simulations and real-world experiments. The results show that the proposed DInEKF outperforms the standard distributed EKF in terms of both accuracy and consistency

Enhancing Detail Preservation for Customized Text-to-Image Generation: A Regularization-Free Approach

Recent text-to-image generation models have demonstrated impressive capability of generating text-aligned images with high fidelity. However, generating images of novel concept provided by the user input image is still a challenging task. To address this problem, researchers have been exploring various methods for customizing pre-trained text-to-image generation models. Currently, most existing methods for customizing pre-trained text-to-image generation models involve the use of regularization techniques to prevent over-fitting. While regularization will ease the challenge of customization and leads to successful content creation with respect to text guidance, it may restrict the model capability, resulting in the loss of detailed information and inferior performance. In this work, we propose a novel framework for customized text-to-image generation without the use of regularization. Specifically, our proposed framework consists of an encoder network and a novel sampling method which can tackle the over-fitting problem without the use of regularization. With the proposed framework, we are able to customize a large-scale text-to-image generation model within half a minute on single GPU, with only one image provided by the user. We demonstrate in experiments that our proposed framework outperforms existing methods, and preserves more fine-grained details

Entrepreneurial Intentions and Behaviour as the Creation of Business: Based on the Theory of Planned Behaviour Extension Evidence from Polish Universities and Entrepreneurs

The purpose of this research was to analyze the relationships between the factors that influence entrepreneurial Intention (EI), using a modified version of Ajzen’s theory of planned behaviour (TPB), considering the perception of behaviour. This examination depended on participants' demographic characteristics and psycho-social behavioural traits of attitude (ATT), Subjective norm (SN), and perceived behavioural control (PBC). The establishment of a new business entails various forms of action to achieve desired results. This research analyzes entrepreneurship as the creation of business by engaging in rational behaviour to optimize the use of available technologies and financial sources. These activities are not standardized: They emerge from the entrepreneurial imagination, the perception of new opportunities, and innovation. The aim of a business is not just to produce and sell goods or services. A company must determine the appropriate means of providing them and choose the values to be adopted in the procedure of doing so. Companies should also identify the actions to be taken so that principals or employees incorporate these values into their activities and establish the character that will permit them to regards options and make correct decisions in keeping with the business’s goals

Unsupervised Out-of-Distribution Detection with Diffusion Inpainting

Unsupervised out-of-distribution detection (OOD) seeks to identify out-of-domain data by learning only from unlabeled in-domain data. We present a novel approach for this task - Lift, Map, Detect (LMD) - that leverages recent advancement in diffusion models. Diffusion models are one type of generative models. At their core, they learn an iterative denoising process that gradually maps a noisy image closer to their training manifolds. LMD leverages this intuition for OOD detection. Specifically, LMD lifts an image off its original manifold by corrupting it, and maps it towards the in-domain manifold with a diffusion model. For an out-of-domain image, the mapped image would have a large distance away from its original manifold, and LMD would identify it as OOD accordingly. We show through extensive experiments that LMD achieves competitive performance across a broad variety of datasets. Code can be found at https://github.com/zhenzhel/lift_map_detect.Comment: ICML 202

Trichlorophenyl-benzoxime induces apoptosis in human colon carcinoma cells via activation of mitochondria dependent pathway

Purpose: To determine the apoptotic effect of trichlorophenyl-benzoxime (TCPB) on colorectal cancer (CRC) cells, and to elucidate the mechanism of action. Methods: Colon carcinoma cell lines (DLD-1 and HT-29) were used in this study. The cells were cultured in Dulbecco's modified Eagle's medium (DMEM) supplemented with 10 % fetal bovine serum (FBS) and 1 % penicillin/streptomycin at 37 ˚C in an atmosphere of 5 % CO2 and 95 % air. When the cells attained 60 - 70 % confluency, they were treated with serum-free medium and graded concentrations of TCPB (1.0 – 6.0 μM) for 24 h. Cell viability and apoptosis were assessed using 3-(4, 5-dimethylthiazol-2-yl) 2, 5-diphenyltetrazolium bromide (MTT) and flow cytometric assays, respectively. Western blotting and 2', 7' dichlorofluorescein diacetate (DCFH DA) assays were used for the determination of expression levels of apoptotic proteins, and levels of reactive oxygen species (ROS), respectively. Results: Treatment of DLD-1 and HT-29 cells with TCPB led to significant and dose-dependent reductions in their viability, as well as significant and dose-dependent increases in the number of apoptotic cells (p < 0.05). Treatment of HT-29 cells with TCPB led to significant increases in the population of cells in the G0/G1 phase, but significant reduction of cell proportion in S and G2/M phases (p < 0.05). It also significantly and dose-dependently upregulated the expressions of caspase-3 and bax, down-regulation of the expression of bcl-2 (p < 0.05). TCPB treatment upregulated the expressions of p53, cytochrome c (cyt c), procaspase-3, and procaspase-9, but down-regulated the expression of pAkt dose-dependently (p < 0.05). The expression of Akt in HT-29 cells was not significantly affected by TCPB (p > 0.05). However, TCPB significantly enhanced the cleavage of PARP1, and significantly and dose-dependently increased the levels of ROS in HT-29 cells (p < 0.05). Conclusion: These results suggest that TCPB exerts apoptotic effect on CRC cells via activation of mitochondria-dependent pathway, and thus can be suitably developed for the management of colon cancer

A Graph-Native Query Optimization Framework

Graph queries that combine pattern matching with relational operations, referred as PatRelQuery, are widely used in many real-world applications. It allows users to identify arbitrary patterns in a graph and further perform in-depth relational analysis on the results. To effectively support PatRelQuery, two key challenges need to be addressed: (1) how to optimize PatRelQuery in a unified framework, and (2) how to handle the arbitrary type constraints in patterns in PatRelQuery. In this paper, we present a graph-native query optimization framework named GOpt, to tackle these issues. GOpt is built on top of a unified intermediate representation (IR) that is capable of capturing both graph and relational operations, thereby streamlining the optimization of PatRelQuery. To handle the arbitrary type constraints, GOpt employs an automatic type inference approach to identify implicit type constraints. Additionally, GOpt introduces a graph-native optimizer, which encompasses an extensive collection of optimization rules along with cost-based techniques tailored for arbitrary patterns, to optimize PatRelQuery. Through comprehensive experiments, we demonstrate that GOpt can achieve significant query performance improvements, in both crafted benchmarks and real-world applications